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Lajavaness
/
bilingual-document-embedding

Sentence Similarity
sentence-transformers
Safetensors
Transformers
French
bilingual
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community
2

Instructions to use Lajavaness/bilingual-document-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Lajavaness/bilingual-document-embedding with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Lajavaness/bilingual-document-embedding", trust_remote_code=True)
    
    sentences = [
        "C'est une personne heureuse",
        "C'est un chien heureux",
        "C'est une personne très heureuse",
        "Aujourd'hui est une journée ensoleillée"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Transformers

    How to use Lajavaness/bilingual-document-embedding with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Lajavaness/bilingual-document-embedding", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
bilingual-document-embedding
2.29 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 9 commits
dangvantuan's picture
dangvantuan
Update README.md
97284a0 verified over 1 year ago
  • .ipynb_checkpoints
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  • .gitattributes
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
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  • model.safetensors
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  • modules.json
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  • sentence_bert_config.json
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  • sentencepiece.bpe.model
    5.07 MB
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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